Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments

In this paper we propose a particle filter localization approach, based on stereo visual odometry (VO) and semantic information from indoor environments, for mini-aerial robots. The prediction stage of the particle filter is performed using the 3D pose of the aerial robot estimated by the stereo VO algorithm. This predicted 3D pose is updated using inertial as well as semantic measurements. The algorithm processes semantic measurements in two phases; firstly, a pre-trained deep learning (DL) based object detector is used for real time object detections in the RGB spectrum. Secondly, from the corresponding 3D point clouds of the detected objects, we segment their dominant horizontal plane and estimate their relative position, also augmenting a prior map with new detections. The augmented map is then used in order to obtain a drift free pose estimate of the aerial robot. We validate our approach in several real flight experiments where we compare it against ground truth and a state of the art visual SLAM approach

Reverse Engineering of Mechanical Parts: a Template-Based Approach

Abstract Template-Based reverse engineering approaches represent a relatively poorly explored strategy in the field of CAD reconstruction from polygonal models. Inspired by recent works suggesting the possibility/opportunity of exploiting a parametric description (i.e. CAD template) of the object to be reconstructed in order to retrieve a meaningful digital representation, a novel reverse engineering approach for the reconstruction of CAD models starting from 3D mesh data is proposed. The reconstruction process is performed relying on a CAD template, whose feature tree and geometric constraints are defined according to the a priori information on the physical object. The CAD template is fitted upon the mesh data, optimizing its dimensional parameters and positioning/orientation by means of a particle swarm optimization algorithm. As a result, a parametric CAD model that perfectly fulfils the imposed geometric relations is produced and a feature tree, defining an associative modelling history, is available to the reverse engineer. The proposed implementation exploits a cooperation between a CAD software package (Siemens NX) and a numerical software environment (MATLAB). Five reconstruction tests, covering both synthetic and real-scanned mesh data, are presented and discussed in the manuscript; the results are finally compared with models generated by state of the art reverse engineering software and key aspects to be addressed in future work are hinted at. Highlights A novel CAD reconstruction method fitting a CAD template model to mesh data. A feature-based parametric-associative modelling history is retrieved. Fitting process is controlled by a Particle Swarm Optimization algorithm. Accuracy of reconstructed models is comparable/better than state of the art results. Computational costs and required time are at the moment considerable